Deep Learning XOR Revisited Example

[smithjackson35]

I have looked over the code in the repository and still have not found a difference in my code that would cause it to break. I have reworked the entire chapter up to this point and still can not figure it out. Below is the error message and my code in its entirety is attached.

In [121]: with tqdm.trange(3000) as t:
...: for epoch in t:
...: epoch_loss = 0.0
...: for x, y in zip(xs, ys):
...: predicted = net.forward(x)
...: epoch_loss += loss.loss(predicted, y)
...: gradient = loss.gradient(predicted, y)
...: net.backward(gradient)
...: optimizer.step(net)
...: t.set_description(f"xor loss {epoch_loss:.3f}")
...:
0%| | 0/3000 [00:00<?, ?it/s]

NotImplementedError Traceback (most recent call last)
in
3 epoch_loss = 0.0
4 for x, y in zip(xs, ys):
----> 5 predicted = net.forward(x)
6 epoch_loss += loss.loss(predicted, y)
7 gradient = loss.gradient(predicted, y)

in forward(self, input)
10 """Just forward the input through the layers in order."""
11 for layer in self.layers:
---> 12 input = layer.forward(input)
13 return input
14 def backward(self, gradient):

in forward(self, input)
6 Note the lack of types. We're not going to be perscriptive about what kinds of inputs layers can take and whats kinds of outputs they can return
7 """
----> 8 raise NotImplementedError
9 def backward(self, gradient):
10 """

NotImplementedError:
dsfs_deeplearningissue.txt

[smithjackson35]

I see where i defined some of the Linear class methods out side of the class block. I went back and correct that and still receive the same error